Complex systems with a multitude of dynamic variables require complicated decision-making methods in order to yield a desired output. Systems having thousands of dynamic variables, for example, may require the implementation of complicated mathematical techniques to define system operation and control its outputs. In many complex systems, these dynamic variables are evaluated by an operator who determines variable changes based on crude evaluations of the complex system. Accordingly, human error and personal interpretation may cause undesired changes in the system output.
Additionally, complex systems may have countless decision variables, which are variables that are adjusted in order to yield a desired output. Each variable represents various aspects of the system, wherein some variables may be discrete and others continuous. The adjustment of decision variables to yield a desired output is challenging and may lead to a rigid logical construct of the rules that describe the system. This rigid logical construct of the rules may not comport with desired control or input to the system, thereby leading to undesirable automated feedback for such systems.
For these and other reasons, improvements in existing complex system controls are desirable.